Richness diversity
betadisper(beta_q0n$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 0.51463 0.102926 11.076 999 0.001 ***
Residuals 86 0.79917 0.009293
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 1.0000e-03 3.0000e-03 1.0000e-03 1.0000e-03 0.012
Brazil 8.1559e-05 9.6000e-02 7.7000e-02 9.1000e-01 0.132
CaboVerde 6.2922e-04 1.1254e-01 1.0000e-03 2.7000e-02 0.941
Spain 1.7363e-12 7.3505e-02 3.5940e-05 2.7000e-02 0.002
Denmark 1.3206e-07 9.0556e-01 2.9720e-02 2.3526e-02 0.049
Malaysia 9.6128e-03 1.3730e-01 9.2726e-01 4.6570e-04 5.9273e-02
adonis2(beta_q0n$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q0n$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()
tinytable_haqvhsrb2vfhkkqgn2im
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| location |
5 |
6.57607 |
0.2442751 |
5.559605 |
0.001 |
| Residual |
86 |
20.34468 |
0.7557249 |
NA |
NA |
| Total |
91 |
26.92075 |
1.0000000 |
NA |
NA |
pairwise.adonis(beta_q0n$S, sample_metadata$location, perm = 999)
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 2.0520609 7.280171 0.19528265 0.001 0.015 .
2 Aruba vs CaboVerde 1 1.0026264 3.213494 0.09975614 0.001 0.015 .
3 Aruba vs Denmark 1 1.9513181 7.018855 0.19486614 0.001 0.015 .
4 Aruba vs Malaysia 1 1.1650328 3.678697 0.11257172 0.001 0.015 .
5 Aruba vs Spain 1 2.0433431 8.167535 0.21974917 0.001 0.015 .
6 Brazil vs CaboVerde 1 2.1090004 9.164611 0.24013374 0.001 0.015 .
7 Brazil vs Denmark 1 0.5703370 2.907951 0.09113563 0.002 0.030 .
8 Brazil vs Malaysia 1 0.9283278 3.953419 0.11996993 0.001 0.015 .
9 Brazil vs Spain 1 0.6343957 3.769479 0.11503016 0.001 0.015 .
10 CaboVerde vs Denmark 1 1.8099512 8.070070 0.22373314 0.001 0.015 .
11 CaboVerde vs Malaysia 1 1.2143821 4.593887 0.14094322 0.001 0.015 .
12 CaboVerde vs Spain 1 1.8141430 9.281722 0.24896172 0.001 0.015 .
13 Denmark vs Malaysia 1 1.0124763 4.418610 0.13629855 0.001 0.015 .
14 Denmark vs Spain 1 0.5305346 3.310771 0.10573905 0.001 0.015 .
15 Malaysia vs Spain 1 0.8450695 4.218747 0.13094076 0.001 0.015 .
#pdf("figures/beta_q0_loca.pdf",width=9, height=5)
beta_q0n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)

Neutral diversity
betadisper(beta_q1n$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 0.2728 0.054560 3.7079 999 0.005 **
Residuals 86 1.2654 0.014715
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 9.0000e-03 3.0000e-03 1.0000e-03 1.0000e-03 0.001
Brazil 9.2383e-03 7.4800e-01 2.0500e-01 6.6700e-01 0.878
CaboVerde 7.7521e-04 7.7033e-01 3.0600e-01 8.4800e-01 0.902
Spain 1.6646e-05 2.2327e-01 3.0424e-01 3.9000e-01 0.274
Denmark 3.3254e-04 6.6322e-01 8.7642e-01 3.7291e-01 0.773
Malaysia 1.1422e-03 8.5989e-01 8.9763e-01 2.4754e-01 7.7435e-01
adonis2(beta_q1n$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1n$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()
tinytable_o3o0iba2cvfctnzpidje
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| location |
5 |
5.709562 |
0.221304 |
4.88821 |
0.001 |
| Residual |
86 |
20.090068 |
0.778696 |
NA |
NA |
| Total |
91 |
25.799630 |
1.000000 |
NA |
NA |
pairwise.adonis(beta_q1n$S, sample_metadata$location, perm = 999)
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 1.3739907 4.789154 0.13766227 0.001 0.015 .
2 Aruba vs CaboVerde 1 1.0636589 3.806728 0.11603497 0.001 0.015 .
3 Aruba vs Denmark 1 1.6037782 5.813780 0.16699651 0.001 0.015 .
4 Aruba vs Malaysia 1 1.2584980 4.466640 0.13346545 0.001 0.015 .
5 Aruba vs Spain 1 1.4382789 5.558030 0.16083181 0.001 0.015 .
6 Brazil vs CaboVerde 1 1.5901673 7.027154 0.19505161 0.001 0.015 .
7 Brazil vs Denmark 1 0.6954159 3.122217 0.09719804 0.003 0.045 .
8 Brazil vs Malaysia 1 0.7313843 3.199010 0.09935119 0.001 0.015 .
9 Brazil vs Spain 1 0.4007935 1.948927 0.06297236 0.031 0.465
10 CaboVerde vs Denmark 1 1.8198218 8.556140 0.23405480 0.001 0.015 .
11 CaboVerde vs Malaysia 1 1.3171418 6.019861 0.17695136 0.001 0.015 .
12 CaboVerde vs Spain 1 1.5415454 7.905417 0.22017337 0.001 0.015 .
13 Denmark vs Malaysia 1 1.0691578 4.970172 0.15074753 0.001 0.015 .
14 Denmark vs Spain 1 0.5766622 3.014217 0.09718823 0.006 0.090
15 Malaysia vs Spain 1 0.6446972 3.265586 0.10444666 0.001 0.015 .
#pdf("figures/beta_q1n_loca.pdf",width=9, height=5)
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)

Phylogenetic diversity
betadisper(beta_q1p$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 0.2152 0.04304 2.1047 999 0.073 .
Residuals 86 1.7587 0.02045
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 0.06400000 0.05500000 0.00100000 0.09900000 0.002
Brazil 0.06236518 0.87200000 0.33000000 0.66400000 0.669
CaboVerde 0.05279004 0.86912737 0.20400000 0.76400000 0.532
Spain 0.00060645 0.32391452 0.20693940 0.10200000 0.517
Denmark 0.09811567 0.63811338 0.73976492 0.10108967 0.309
Malaysia 0.00519311 0.67845753 0.52055144 0.50696000 0.31212601
adonis2(beta_q1p$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1p$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()
tinytable_nwvt1dmxuhi2q76nk369
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| location |
5 |
2.690791 |
0.2683828 |
6.309563 |
0.001 |
| Residual |
86 |
7.335152 |
0.7316172 |
NA |
NA |
| Total |
91 |
10.025944 |
1.0000000 |
NA |
NA |
pairwise.adonis(beta_q1p$S, sample_metadata$location, perm = 999)
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 0.64714035 6.1133065 0.16928127 0.001 0.015 .
2 Aruba vs CaboVerde 1 0.56229778 5.3504131 0.15575979 0.001 0.015 .
3 Aruba vs Denmark 1 1.03758657 9.5591181 0.24790811 0.001 0.015 .
4 Aruba vs Malaysia 1 0.48047063 5.0352414 0.14794199 0.002 0.030 .
5 Aruba vs Spain 1 0.60047832 6.7919744 0.18976250 0.001 0.015 .
6 Brazil vs CaboVerde 1 0.70961443 8.1023431 0.21837821 0.001 0.015 .
7 Brazil vs Denmark 1 0.32742702 3.5968626 0.11034383 0.022 0.330
8 Brazil vs Malaysia 1 0.19570506 2.5119802 0.07971509 0.026 0.390
9 Brazil vs Spain 1 0.06456161 0.9106384 0.03044530 0.459 1.000
10 CaboVerde vs Denmark 1 1.07120011 11.9405505 0.29895808 0.001 0.015 .
11 CaboVerde vs Malaysia 1 0.50032095 6.5728056 0.19011490 0.002 0.030 .
12 CaboVerde vs Spain 1 0.82104026 11.9236999 0.29866220 0.001 0.015 .
13 Denmark vs Malaysia 1 0.54258755 6.8084757 0.19559821 0.001 0.015 .
14 Denmark vs Spain 1 0.24626102 3.3999417 0.10827860 0.033 0.495
15 Malaysia vs Spain 1 0.26932060 4.5771932 0.14050299 0.001 0.015 .
#pdf("figures/beta_q1p_loca.pdf",width=9, height=5)
beta_q1n$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)

Functional diversity
betadisper(beta_q1f$S, sample_metadata$location) %>% permutest(., pairwise=TRUE)
Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999
Response: Distances
Df Sum Sq Mean Sq F N.Perm Pr(>F)
Groups 5 1.5016 0.300325 9.1732 999 0.001 ***
Residuals 86 2.8156 0.032739
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
Aruba Brazil CaboVerde Spain Denmark Malaysia
Aruba 2.0000e-03 6.0000e-01 2.0000e-03 1.0000e-03 0.494
Brazil 8.6955e-04 1.0000e-03 6.7300e-01 2.5400e-01 0.003
CaboVerde 5.8936e-01 9.7727e-04 3.0000e-03 1.0000e-03 0.879
Spain 1.5125e-03 6.8358e-01 1.5804e-03 4.6000e-02 0.004
Denmark 1.7539e-04 2.3957e-01 9.2505e-05 3.3658e-02 0.001
Malaysia 4.9615e-01 1.0071e-03 8.8292e-01 1.5959e-03 7.4748e-05
adonis2(beta_q1f$S ~ location,
data = sample_metadata %>% arrange(match(sample,labels(beta_q1f$S))),
permutations = 999) %>%
broom::tidy() %>%
tt()
tinytable_ap1s72pyl0acpd333eg6
| term |
df |
SumOfSqs |
R2 |
statistic |
p.value |
| location |
5 |
8.815599 |
0.5844961 |
24.19552 |
0.001 |
| Residual |
86 |
6.266793 |
0.4155039 |
NA |
NA |
| Total |
91 |
15.082391 |
1.0000000 |
NA |
NA |
pairwise.adonis(beta_q1f$S, sample_metadata$location, perm = 999)
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
1 Aruba vs Brazil 1 3.77109355 40.6261637 0.575228238 0.001 0.015 .
2 Aruba vs CaboVerde 1 0.81599223 5.5801053 0.161367505 0.023 0.345
3 Aruba vs Denmark 1 4.54372380 50.8948430 0.637022880 0.001 0.015 .
4 Aruba vs Malaysia 1 0.87672195 6.0397240 0.172367910 0.010 0.150
5 Aruba vs Spain 1 3.49955224 36.9984243 0.560595570 0.001 0.015 .
6 Brazil vs CaboVerde 1 1.79573937 27.7696018 0.489163230 0.001 0.015 .
7 Brazil vs Denmark 1 0.27026868 35.0545409 0.547260825 0.001 0.015 .
8 Brazil vs Malaysia 1 1.69391038 26.6369446 0.478763612 0.001 0.015 .
9 Brazil vs Spain 1 -0.01258568 -0.9666596 -0.034482498 0.943 1.000
10 CaboVerde vs Denmark 1 2.85656348 47.6207158 0.629731090 0.001 0.015 .
11 CaboVerde vs Malaysia 1 -0.02164970 -0.1836835 -0.006603446 0.986 1.000
12 CaboVerde vs Spain 1 1.56212224 23.8545897 0.460028512 0.001 0.015 .
13 Denmark vs Malaysia 1 2.82610262 48.0023359 0.631590271 0.001 0.015 .
14 Denmark vs Spain 1 0.38029019 58.5480612 0.676480332 0.001 0.015 .
15 Malaysia vs Spain 1 1.47580731 22.9256154 0.450178466 0.001 0.015 .
#pdf("figures/beta_q1f_loca.pdf",width=9, height=5)
beta_q1f$S %>%
vegan::metaMDS(., trymax = 500, k = 2, trace=0) %>%
vegan::scores() %>%
as_tibble(., rownames = "sample") %>%
left_join(sample_metadata, by = "sample") %>%
group_by(location) %>%
mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
ungroup() %>%
ggplot(aes(x = NMDS1, y = NMDS2, color = location, fill = location)) +
geom_point(size = 4) +
# stat_ellipse(aes(color = beta_q1n_nmds$Groups))+
geom_segment(aes(x = x_cen, y = y_cen, xend = NMDS1, yend = NMDS2), alpha = 0.9, show.legend = FALSE) +
scale_color_manual(values = location_colors)+
theme_classic() +
theme(
axis.text.x = element_text(size = 12),
axis.text.y = element_text(size = 12),
axis.title = element_text(size = 20, face = "bold"),
axis.text = element_text(face = "bold", size = 18),
panel.background = element_blank(),
axis.line = element_line(size = 0.5, linetype = "solid", colour = "black"),
legend.text = element_text(size = 16),
legend.title = element_text(size = 18),
legend.position = "right", legend.box = "vertical"
)
